Conducting Network Intrusion Detection with Enhanced Deep Learning

• Main Authors: Dong-Ye Wu, 吳東燁
• Other Authors: Po-jen Chuang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/vfkdnh

碩士 === 淡江大學 === 電機工程學系碩士班 === 107 === Abstract: With the rapid development of information and communication in recent years, the amount of transmission used by people increase. In addition, a large number of internet of things devices are entering the market, which also results in a large amount of data transmission. With the generation of these flows, intrusion detection systems will be challenged. In recent years, research has found that the challenges encountered by intrusion detection systems can be divided into the following categories: (1) the volume of data both stored and passing through networks continues to increase; (2) the depth of intrusion detection systems; (3) a variety of protocols and data. These three problems are the main challenges of intrusion detection systems in recent years. The first reason for the large amount of data generated in the network is the rapid development of the information and communication industry in recent years. The development of internet of things devices is also increasingly diversified, resulting in a large number of devices into the market. As a result, a large amount of information is transmitted over the network, and the data in the network becomes even larger. This will impose a burden on the intrusion detection system, because a large number of traffics in the transmission process, needs to be more intensive to deal with a large number of traffics. Even with the improvement in computing performance, it is still not enough to cope with the increasing traffic. In terms of the detection depth of intrusion detection system, in order to improve the effectiveness and accuracy of intrusion detection system, intrusion detection system can no longer rely on some simple or obvious features to identify whether the traffic is an attack. Must be able to observe and detect in greater depth, which means that intrusion detection system needs to observe more characteristics. This paper proposes the method of deep learning to solve the imbalance of intrusion detection data set. We use deep variational autoencoders to generate new data to balance the unbalanced dataset. The balanced data can reduce the deviation of classifier in training because of the imbalance of data. In addition, we used a balanced dataset to train the deep autoencoder. By using the depth autoencoder to compress the features of the essential features, we can remove the redundant parts of the features. This will enable us to classify data more accurately. Experimental results show that classification accuracy is better when balanced datasets are used. Coupled with the use of the balanced dataset trained by the feature compression model, we can get better accuracy. Compared to unbalanced datasets, we have better robustness against unknown attacks. We can also solve the problem of over-fitting in the training of the model caused by the imbalance of various types of data. This will ensure that our intrusion detection model will not misjudge new types of data because they are not in the training data set.